Introduction


DADA2 est un pipeline bio-informatique (Callahan et al., 2016). Il consiste en une série d’étapes permettant de filtrer les séquences brutes obtenues grâce au séquençage Illumina. La dernière étape vise à obtenir la taxonomie des séquences ayant été filtrées en vue d’étudier la communauté microbienne.

DADA2 a deux particularités qui le distingue des autres pipeline courament utilisés. D’une part, il va procéder à modélisation de l’erreur dûe au séquençage ce qui est censé permettre des distinguer les séquences mutantes, des séquences érronées. D’autre part, contrairement à d’autres pipelines comme QIIME ou Mothur, DADA2 ne regroupe pas les séquences similaires à 97% en Unités Taxonomiques Opérationelles (OTUs). Ses Variants de Séquences d’Amplicons (ASVs) ne subissent pas de regroupement si les séqences ne sont pas identiques à 100%. Voir figure ci-dessous.

A l’origine construit pour les séquences de gène marqueur 16S (Bacteria), nous l’utiliserons avec des séquences de gène marqueur ITS (Fungi) provenant d’un séquençage en paire Illumina MiSEQ 2x300 paires de bases. Afin d’accélérer l’exécution de chaques étapes, nous avons sous-échantilloner aléatoirement un jeu de données afin d’avoir 1000 séquences par échantillons.

Enfin, Redde Caesari quae sunt Caesaris : ce tutoriel s’est largement inspiré du propre tutoriel de DADA2.

De manière générale avant de débuter ce pipeline, il faut prendre quelques précautions:

  1. Les échantillons doivent être démultiplexés chaque échantillon doit avoir son propre fichier fastq.
  2. En cas de séquençage en paire, les séquences sens et anti-sens doivent être dans deux fichier fastq distincts et être dans le même ordre dans les deux fichiers.
  3. Les nucléotides qui ne font pas partie de l’amplicon (amorces, adaptateurs, bar-code) doivent avoir été retirées. Dans le cas contraire, ils devront être à l’étape de filtrage.
  4. La plupart des fonctions présentées ont une option de multithreading qui permet d’accélerer les temps de calcul en accédant à plusieurs prorcesseurs. Il suffit d’indiquer multithread = TRUE pour l’activer. Attention, cette option ne marche pas sous Windows.


Cette figure extraite de Hugerth et Andersson, 2017 illustre la différence théorique entre OTUs et ASVs. Chaque couleur représente une clade. Les étoiles jaunes indiquent des mutations, les étoiles rouges indiquent des erreurs d’amplification ou de séquençage. La taille de l’espace entre les séquences indique leur regroupement.

This figure taken from Hugerth and Andersson, 2017 illustrates the theoretical difference between OTUs and ASV. Each color represents a clade. Yellow stars indicate mutations, red stars indicate amplification or sequencing errors. The size of the space between the sequences indicates their clustering.


(A) OTUs regroupés à 100 % d’identité.
La moindre variation de séquences provoque la création d’un nouveau groupe. Les séquences mutantes et les séquences erronées sont traitées de la même manière.
(B) OTUs regroupés à 97 % d’identité.
Un regroupement plus large permet de ne plus considérer les séquences erronées, cependant les séquences mutantes seront également regroupées dans le groupe consensus.
(C) ASVs
L’apprentissage du taux d’erreur permet théoriquement de regrouper les séquences erronées avec les séquences consensus. En revanche, les séquences mutantes sont considérées à part entière.

(A) 100 % identity clustered OTUs.
The slightest variation of sequences causes the creation of a new group. The mutant sequences and the erroneous sequences are treated similarly.
(B) 97 % identity clustered OTUs.
A wider grouping allows to no longer consider the erroneous sequences, however the mutant sequences will also be clustered in the consensus group.
(C) ASVs.
Learning the error rates theoretically enables to group the erroneous sequences with the consensus sequences. In contrast, the mutant sequences are considered integrally.


DADA2 is a bioinformatics pipeline created by Callahan et al., 2016. It consists is a series of steps which filter the raw sequences obtained with Illumina sequencing. The final step is to obtain the taxonomy of the sequences that have been filtered in order to study the microbial community.

DADA2 has two major features which distinguishes it from other commonly used pipelines. On one hand, it will proceed to the modeling of the sequencing error which is supposed to make it possible to distinguish mutant sequences from erroneous sequences. On the other hand, unlike other pipelines such as QIIME or Mothur, DADA2 does not cluster 97% similar sequences in Operational Taxonomy Units (OTUs). Its Amplicon Sequence Variants (ASVs) are not grouped if the sequences are not 100% identical. See figure above.

Originally constructed for 16S marker gene sequences (Bacteria), we will use it with ITS marker gene (Fungi) sequences from Illumina MiSEQ 2x300 bp paired-end sequencing. To speed up the execution of each step, we randomly sub-sampled a dataset in order to only keep 1000 sequences per sample. Finally, Redde Caesari quae sunt Caesaris : this tutorial was largely inspired by the original DADA2 tutorial.

In general, before starting this pipeline, we must take some precautions:

  1. Samples must be demultiplexed: split into individual per-sample fastqs
  2. If the sequences are paired-end, the forward and reverse sequences have to be in distinct fastq files but must contain reads in matched order.
  3. The nucleotides which are not part of the amplicon (primers, adapters, bar-code) have to be removed. They can be removed at the filtring step.
  4. Most functions have a multithreading option that allows faster computing time by accessing multiple processors. Just specify multithread = TRUE to enable it. Warning, this option does not work under Windows.



Commençons ! / Let’s start !


Nous allons tout d’abord chargé la librairie DADA2. Vous devriez avoir la denière version: packageVersion('dada2').
Puis nous allons créer une variable (path) indiquant le chemin qui permettra d’accéder aux objets dont nous allons avoir besoin.

First, we’re going to load the DADA2 package. You should have the latest version: packageVersion('dada2'). Then we’re going to create a variable (path) indicating the path which will allow to access the objects required for this pipeline. 

library(dada2); packageVersion("dada2")
## Loading required package: Rcpp
## [1] '1.6.0'
path <- "data/ITS_sub/"

Vérifions ce qu’il y a au bout du chemin… / Let’s check where the path leads to…. 


list.files(path)
##   [1] "filtered_pairedend" "S10_R1.fastq"       "S10_R2.fastq"      
##   [4] "S11_R1.fastq"       "S11_R2.fastq"       "S12_R1.fastq"      
##   [7] "S12_R2.fastq"       "S13_R1.fastq"       "S13_R2.fastq"      
##  [10] "S14_R1.fastq"       "S14_R2.fastq"       "S15_R1.fastq"      
##  [13] "S15_R2.fastq"       "S16_R1.fastq"       "S16_R2.fastq"      
##  [16] "S17_R1.fastq"       "S17_R2.fastq"       "S18_R1.fastq"      
##  [19] "S18_R2.fastq"       "S1_R1.fastq"        "S1_R2.fastq"       
##  [22] "S20_R1.fastq"       "S20_R2.fastq"       "S21_R1.fastq"      
##  [25] "S21_R2.fastq"       "S22_R1.fastq"       "S22_R2.fastq"      
##  [28] "S23_R1.fastq"       "S23_R2.fastq"       "S24_R1.fastq"      
##  [31] "S24_R2.fastq"       "S25_R1.fastq"       "S25_R2.fastq"      
##  [34] "S26_R1.fastq"       "S26_R2.fastq"       "S27_R1.fastq"      
##  [37] "S27_R2.fastq"       "S28_R1.fastq"       "S28_R2.fastq"      
##  [40] "S29_R1.fastq"       "S29_R2.fastq"       "S2_R1.fastq"       
##  [43] "S2_R2.fastq"        "S30_R1.fastq"       "S30_R2.fastq"      
##  [46] "S31_R1.fastq"       "S31_R2.fastq"       "S33_R1.fastq"      
##  [49] "S33_R2.fastq"       "S34_R1.fastq"       "S34_R2.fastq"      
##  [52] "S35_R1.fastq"       "S35_R2.fastq"       "S36_R1.fastq"      
##  [55] "S36_R2.fastq"       "S37_R1.fastq"       "S37_R2.fastq"      
##  [58] "S39_R1.fastq"       "S39_R2.fastq"       "S3_R1.fastq"       
##  [61] "S3_R2.fastq"        "S41_R1.fastq"       "S41_R2.fastq"      
##  [64] "S42_R1.fastq"       "S42_R2.fastq"       "S43_R1.fastq"      
##  [67] "S43_R2.fastq"       "S44_R1.fastq"       "S44_R2.fastq"      
##  [70] "S45_R1.fastq"       "S45_R2.fastq"       "S46_R1.fastq"      
##  [73] "S46_R2.fastq"       "S47_R1.fastq"       "S47_R2.fastq"      
##  [76] "S48_R1.fastq"       "S48_R2.fastq"       "S49_R1.fastq"      
##  [79] "S49_R2.fastq"       "S4_R1.fastq"        "S4_R2.fastq"       
##  [82] "S50_R1.fastq"       "S50_R2.fastq"       "S51_R1.fastq"      
##  [85] "S51_R2.fastq"       "S52_R1.fastq"       "S52_R2.fastq"      
##  [88] "S53_R1.fastq"       "S53_R2.fastq"       "S54_R1.fastq"      
##  [91] "S54_R2.fastq"       "S55_R1.fastq"       "S55_R2.fastq"      
##  [94] "S56_R1.fastq"       "S56_R2.fastq"       "S58_R1.fastq"      
##  [97] "S58_R2.fastq"       "S59_R1.fastq"       "S59_R2.fastq"      
## [100] "S5_R1.fastq"        "S5_R2.fastq"        "S60_R1.fastq"      
## [103] "S60_R2.fastq"       "S61_R1.fastq"       "S61_R2.fastq"      
## [106] "S62_R1.fastq"       "S62_R2.fastq"       "S63_R1.fastq"      
## [109] "S63_R2.fastq"       "S64_R1.fastq"       "S64_R2.fastq"      
## [112] "S65_R1.fastq"       "S65_R2.fastq"       "S66_R1.fastq"      
## [115] "S66_R2.fastq"       "S67_R1.fastq"       "S67_R2.fastq"      
## [118] "S68_R1.fastq"       "S68_R2.fastq"       "S69_R1.fastq"      
## [121] "S69_R2.fastq"       "S6_R1.fastq"        "S6_R2.fastq"       
## [124] "S72_R1.fastq"       "S72_R2.fastq"       "S73_R1.fastq"      
## [127] "S73_R2.fastq"       "S74_R1.fastq"       "S74_R2.fastq"      
## [130] "S75_R1.fastq"       "S75_R2.fastq"       "S76_R1.fastq"      
## [133] "S76_R2.fastq"       "S77_R1.fastq"       "S77_R2.fastq"      
## [136] "S78_R1.fastq"       "S78_R2.fastq"       "S79_R1.fastq"      
## [139] "S79_R2.fastq"       "S7_R1.fastq"        "S7_R2.fastq"       
## [142] "S80_R1.fastq"       "S80_R2.fastq"       "S81_R1.fastq"      
## [145] "S81_R2.fastq"       "S8_R1.fastq"        "S8_R2.fastq"       
## [148] "S9_R1.fastq"        "S9_R2.fastq"

Vous devriez voir les noms des fichiers fastq.

You should see the names of the fastq files.

Nous allons maintenant lire les noms des fichiers fastq, et manipuler leur chaine de charactères variables pour obtenir une liste des fastq sens et antisens. La fonction sort permet d’obtenir le même ordre entre les fastq sens et antisens.

Now, we’re goign to read in the names of the fastq files, and perform some string manipulation to get lists of the forward and reverse fastq files. The sort function ensures forward/reverse reads are in the same order.

fnFs <- sort(list.files(path, pattern="_R1.fastq"))
fnRs <- sort(list.files(path, pattern="_R2.fastq"))

Etant donné que les paires de fichiers fastq sens et antisens appartiennent au même échantillon, nous allons créer une variable qui extrait le nom de cet échantillon. Dans ce cas, nous partons du principe que les noms des fichiers fastq ont un format: SAMPLENAME_XXX.fastq.

Given that the forward/reverse fastq pairs belong to the same sample, we are going to extract the name and save it in a variable. In this case, we assume that the filenames have this type of format: SAMPLENAME_XXX.fastq.

sample.names <- sapply(strsplit(fnFs, "_"), `[`, 1)
sample.names 
##  [1] "S10" "S11" "S12" "S13" "S14" "S15" "S16" "S17" "S18" "S1"  "S20"
## [12] "S21" "S22" "S23" "S24" "S25" "S26" "S27" "S28" "S29" "S2"  "S30"
## [23] "S31" "S33" "S34" "S35" "S36" "S37" "S39" "S3"  "S41" "S42" "S43"
## [34] "S44" "S45" "S46" "S47" "S48" "S49" "S4"  "S50" "S51" "S52" "S53"
## [45] "S54" "S55" "S56" "S58" "S59" "S5"  "S60" "S61" "S62" "S63" "S64"
## [56] "S65" "S66" "S67" "S68" "S69" "S6"  "S72" "S73" "S74" "S75" "S76"
## [67] "S77" "S78" "S79" "S7"  "S80" "S81" "S8"  "S9"

Nous allons maintenant préciser le chemin d’accès aux objets fnFs et fnRS. Specify the full path to the fnFs and fnRs.

fnFs <- file.path(path, fnFs)
fnRs <- file.path(path, fnRs)

Pour les plus maniaques d’entre nous, ce script permet d’ordonner les noms des échantillons.

For the maniacs among us, this script allows to order the names of the samples.

library(gtools)
sample.names <- mixedsort(sample.names) 
fnFs <- mixedsort(fnFs)
fnRs <- mixedsort(fnRs)
sample.names
##  [1] "S1"  "S2"  "S3"  "S4"  "S5"  "S6"  "S7"  "S8"  "S9"  "S10" "S11"
## [12] "S12" "S13" "S14" "S15" "S16" "S17" "S18" "S20" "S21" "S22" "S23"
## [23] "S24" "S25" "S26" "S27" "S28" "S29" "S30" "S31" "S33" "S34" "S35"
## [34] "S36" "S37" "S39" "S41" "S42" "S43" "S44" "S45" "S46" "S47" "S48"
## [45] "S49" "S50" "S51" "S52" "S53" "S54" "S55" "S56" "S58" "S59" "S60"
## [56] "S61" "S62" "S63" "S64" "S65" "S66" "S67" "S68" "S69" "S72" "S73"
## [67] "S74" "S75" "S76" "S77" "S78" "S79" "S80" "S81"


Profil de qualité / Quality profile


Cette première étape permet de visualiser la qualité des séquences grâce au Q score associé à chaque nucléotide.

This first step allows to visualize the sequences quality thanks to the individual Q score of each nucleotide 

plotQualityProfile(fnFs[1]) # 1st Forward sample

plotQualityProfile(fnRs[1]) # 1st Reverse sample

Dans ces figures, la médianne est en vert, et les quartiles en orange pointillé. Ici, nous avons choisi de visualiser le premier échantillon sens (fnFs[1]) et antisens (fnRs[1]), mais il est possible de visualiser plusieurs graphiques en même temps (fnFs[x:y]) ou les aggréger comme ce qui suit.

In these figures, the median is in green and the quartiles are the dotted orange lines. Here we only plotted the first forward and reverse fastq (fnFs[1] and fnRs[1]), but it is possible to plot multiple figures(fnFs[x:y]) or aggregate them as follows. 

plotQualityProfile(fnFs, aggregate = TRUE)

plotQualityProfile(fnRs, aggregate = TRUE)

L’analyse de ces graphiques nous permet de choisir les paramètres de filtrage et de rognage de la prochaine étape. En effet, l’indice de Q score nous renseigne sur la précision du séquençage (voir tableau ci-dessous).

The analysis of these figures helps to choose the filtring and trimming parameters of the next step. The Q score index gives us information on sequencing’s accuracy (see table).

Q score Precision
10 90 %
20 99 %
30 99.9 %
40 99.99 %

Un autre outil plus complet pour évaluer la qualité des séquence: FastQC.

Another tool for evaluating sequence quality: FastQC.

Filtrage et tronquage / Filtering and Trimming


Tout d’abord nous allons créer un dossier (filtered_pairedend) et des objets (filtFs et filtRs) pour stoquer les séquences filtrées.

First we will create a directoy (filtered_pairedend) and objects (filtFs and filtRs) to store the filtered sequences.

filt_path <- file.path(path, "filtered_pairedend") 
filtFs <- file.path(filt_path, paste0(sample.names, "_F_filt.fastq.gz"))
filtRs <- file.path(filt_path, paste0(sample.names, "_R_filt.fastq.gz"))

Procédons avec la fonction filterAndTrim, sa sortie sera stocké dans l’objet out.

Let’s procede with the filterAndTrim function, its output will be stored in the out object 

out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs, 
                     truncQ=6,
                     truncLen = c(280,280),
                     trimLeft=c(18,20),
                     maxEE=c(2,2))
## Creating output directory: data/ITS_sub//filtered_pairedend
                     #multithread=TRUE)

En premier lieu, la fonction a besoin des séquences non filtrées (fnFs et FnRs) ainsi que les noms des objets des séquences filtrées (filtFs et filtRs). Plusieurs paramètres peuvent ensuite être modifiés à notre guise:

D’autres paramètres peuvent également être modifiés, ils sont accessibles à la page d’aide de la fonction: ?filterAndTrim.


First, the function needs the unfiltered sequences (fnFs and FnRs) as well as the names of the objects of the filtered sequences (filtFs and filtRs). Several parameters can then be modified as we wish:

Other settings can also be changed, they are accessible on the help page of the function : ?FilterAndTrim.


Visualisation du filtrage / Filtering visualization

pourc <- cbind((out[,2]/out[,1])*100) # Percentage filtered sequence / non-filtered sequence
pourc_disc <- cbind(out, pourc) # combines out and pourc
pourc_disc 
##              reads.in reads.out     
## S1_R1.fastq      1000       595 59.5
## S2_R1.fastq      1000       482 48.2
## S3_R1.fastq      1000       556 55.6
## S4_R1.fastq      1000       425 42.5
## S5_R1.fastq      1000       387 38.7
## S6_R1.fastq      1000       619 61.9
## S7_R1.fastq      1000       521 52.1
## S8_R1.fastq      1000       423 42.3
## S9_R1.fastq      1000       492 49.2
## S10_R1.fastq     1000       366 36.6
## S11_R1.fastq     1000       432 43.2
## S12_R1.fastq     1000       546 54.6
## S13_R1.fastq     1000       417 41.7
## S14_R1.fastq     1000       229 22.9
## S15_R1.fastq     1000       512 51.2
## S16_R1.fastq     1000       481 48.1
## S17_R1.fastq     1000       634 63.4
## S18_R1.fastq     1000       607 60.7
## S20_R1.fastq     1000       614 61.4
## S21_R1.fastq     1000       598 59.8
## S22_R1.fastq     1000       539 53.9
## S23_R1.fastq     1000       561 56.1
## S24_R1.fastq     1000       599 59.9
## S25_R1.fastq     1000       598 59.8
## S26_R1.fastq     1000       567 56.7
## S27_R1.fastq     1000       650 65.0
## S28_R1.fastq     1000       607 60.7
## S29_R1.fastq     1000       526 52.6
## S30_R1.fastq     1000       551 55.1
## S31_R1.fastq     1000       571 57.1
## S33_R1.fastq     1000       482 48.2
## S34_R1.fastq     1000       644 64.4
## S35_R1.fastq     1000       558 55.8
## S36_R1.fastq     1000       453 45.3
## S37_R1.fastq     1000       446 44.6
## S39_R1.fastq     1000       521 52.1
## S41_R1.fastq     1000       517 51.7
## S42_R1.fastq     1000       447 44.7
## S43_R1.fastq     1000       500 50.0
## S44_R1.fastq     1000       369 36.9
## S45_R1.fastq     1000       359 35.9
## S46_R1.fastq     1000       540 54.0
## S47_R1.fastq     1000       501 50.1
## S48_R1.fastq     1000       391 39.1
## S49_R1.fastq     1000       516 51.6
## S50_R1.fastq     1000       422 42.2
## S51_R1.fastq     1000       567 56.7
## S52_R1.fastq     1000       521 52.1
## S53_R1.fastq     1000       599 59.9
## S54_R1.fastq     1000       472 47.2
## S55_R1.fastq     1000       571 57.1
## S56_R1.fastq     1000       416 41.6
## S58_R1.fastq     1000       525 52.5
## S59_R1.fastq     1000       559 55.9
## S60_R1.fastq     1000       593 59.3
## S61_R1.fastq     1000       537 53.7
## S62_R1.fastq     1000       470 47.0
## S63_R1.fastq     1000       644 64.4
## S64_R1.fastq     1000       595 59.5
## S65_R1.fastq     1000       561 56.1
## S66_R1.fastq     1000       551 55.1
## S67_R1.fastq     1000       544 54.4
## S68_R1.fastq     1000       532 53.2
## S69_R1.fastq     1000       372 37.2
## S72_R1.fastq     1000       558 55.8
## S73_R1.fastq     1000       581 58.1
## S74_R1.fastq     1000       431 43.1
## S75_R1.fastq     1000       356 35.6
## S76_R1.fastq     1000       403 40.3
## S77_R1.fastq     1000       387 38.7
## S78_R1.fastq     1000       400 40.0
## S79_R1.fastq     1000       560 56.0
## S80_R1.fastq     1000       457 45.7
## S81_R1.fastq     1000       594 59.4
(mean(out[,2])/mean(out[,1]))*100 # Mean percentage 
## [1] 50.98243


Près de la moitié des séquences n’ont pas passé nos paramètres de filtrage!

Almost half of the reads didn’t pass through the filtering parameters!


CHALLENGE

Tracer le profil de qualité du premier échantillon une fois filtré et le comparer à son profil de qualité non-filtré. Utiliser la fonction plotQualityProfile.
Draw the quality profile of the first sample once filtered and compare it to its unfiltered quality profile. Use the plotQualityProfile function.

D’autres outils de filtrage existent tel que : Trimmomatic.

Other filtering tools exist like: Trimmomatic.


Apprentissage des taux d’erreur / Error Rates Learning


Cet étape consiste à estimer le taux d’erreur de séquençage. Son but est de permettre de différencier les séquences mutantes et les séquences érronées.Le modèle d’erreur est calculé en alternant l’estimation des taux d’erreur et l’inférence de la composition de l’échantillon jusqu’à ce qu’ils convergent vers une solution cohérente.

This step consist in estimating the error rates due to sequencing. Its purpose is to differentiate between mutant sequences and false sequences. The error model is computed by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. 

errF <- learnErrors(filtFs)
## Initializing error rates to maximum possible estimate.
## Sample 1 - 595 reads in 364 unique sequences.
## Sample 2 - 482 reads in 294 unique sequences.
## Sample 3 - 556 reads in 363 unique sequences.
## Sample 4 - 425 reads in 278 unique sequences.
## Sample 5 - 387 reads in 280 unique sequences.
## Sample 6 - 619 reads in 348 unique sequences.
## Sample 7 - 521 reads in 328 unique sequences.
## Sample 8 - 423 reads in 221 unique sequences.
## Sample 9 - 492 reads in 317 unique sequences.
## Sample 10 - 366 reads in 238 unique sequences.
## Sample 11 - 432 reads in 298 unique sequences.
## Sample 12 - 546 reads in 344 unique sequences.
## Sample 13 - 417 reads in 324 unique sequences.
## Sample 14 - 229 reads in 162 unique sequences.
## Sample 15 - 512 reads in 319 unique sequences.
## Sample 16 - 481 reads in 341 unique sequences.
## Sample 17 - 634 reads in 378 unique sequences.
## Sample 18 - 607 reads in 344 unique sequences.
## Sample 19 - 614 reads in 365 unique sequences.
## Sample 20 - 598 reads in 387 unique sequences.
## Sample 21 - 539 reads in 345 unique sequences.
## Sample 22 - 561 reads in 344 unique sequences.
## Sample 23 - 599 reads in 384 unique sequences.
## Sample 24 - 598 reads in 394 unique sequences.
## Sample 25 - 567 reads in 316 unique sequences.
## Sample 26 - 650 reads in 294 unique sequences.
## Sample 27 - 607 reads in 339 unique sequences.
## Sample 28 - 526 reads in 365 unique sequences.
## Sample 29 - 551 reads in 337 unique sequences.
## Sample 30 - 571 reads in 375 unique sequences.
## Sample 31 - 482 reads in 287 unique sequences.
## Sample 32 - 644 reads in 388 unique sequences.
## Sample 33 - 558 reads in 303 unique sequences.
## Sample 34 - 453 reads in 277 unique sequences.
## Sample 35 - 446 reads in 318 unique sequences.
## Sample 36 - 521 reads in 364 unique sequences.
## Sample 37 - 517 reads in 322 unique sequences.
## Sample 38 - 447 reads in 327 unique sequences.
## Sample 39 - 500 reads in 326 unique sequences.
## Sample 40 - 369 reads in 275 unique sequences.
## Sample 41 - 359 reads in 251 unique sequences.
## Sample 42 - 540 reads in 372 unique sequences.
## Sample 43 - 501 reads in 289 unique sequences.
## Sample 44 - 391 reads in 277 unique sequences.
## Sample 45 - 516 reads in 323 unique sequences.
## Sample 46 - 422 reads in 282 unique sequences.
## Sample 47 - 567 reads in 379 unique sequences.
## Sample 48 - 521 reads in 351 unique sequences.
## Sample 49 - 599 reads in 371 unique sequences.
## Sample 50 - 472 reads in 315 unique sequences.
## Sample 51 - 571 reads in 386 unique sequences.
## Sample 52 - 416 reads in 272 unique sequences.
## Sample 53 - 525 reads in 323 unique sequences.
## Sample 54 - 559 reads in 272 unique sequences.
## Sample 55 - 593 reads in 370 unique sequences.
## Sample 56 - 537 reads in 357 unique sequences.
## Sample 57 - 470 reads in 282 unique sequences.
## Sample 58 - 644 reads in 391 unique sequences.
## Sample 59 - 595 reads in 370 unique sequences.
## Sample 60 - 561 reads in 350 unique sequences.
## Sample 61 - 551 reads in 373 unique sequences.
## Sample 62 - 544 reads in 343 unique sequences.
## Sample 63 - 532 reads in 338 unique sequences.
## Sample 64 - 372 reads in 232 unique sequences.
## Sample 65 - 558 reads in 369 unique sequences.
## Sample 66 - 581 reads in 347 unique sequences.
## Sample 67 - 431 reads in 327 unique sequences.
## Sample 68 - 356 reads in 230 unique sequences.
## Sample 69 - 403 reads in 277 unique sequences.
## Sample 70 - 387 reads in 281 unique sequences.
## Sample 71 - 400 reads in 275 unique sequences.
## Sample 72 - 560 reads in 373 unique sequences.
## Sample 73 - 457 reads in 331 unique sequences.
## Sample 74 - 594 reads in 362 unique sequences.
##    selfConsist step 2 
##    selfConsist step 3 
##    selfConsist step 4 
## Convergence after  4  rounds.
## Total reads used:  37727
errR <- learnErrors(filtRs)
## Initializing error rates to maximum possible estimate.
## Sample 1 - 595 reads in 340 unique sequences.
## Sample 2 - 482 reads in 280 unique sequences.
## Sample 3 - 556 reads in 296 unique sequences.
## Sample 4 - 425 reads in 312 unique sequences.
## Sample 5 - 387 reads in 252 unique sequences.
## Sample 6 - 619 reads in 320 unique sequences.
## Sample 7 - 521 reads in 299 unique sequences.
## Sample 8 - 423 reads in 247 unique sequences.
## Sample 9 - 492 reads in 299 unique sequences.
## Sample 10 - 366 reads in 273 unique sequences.
## Sample 11 - 432 reads in 284 unique sequences.
## Sample 12 - 546 reads in 325 unique sequences.
## Sample 13 - 417 reads in 329 unique sequences.
## Sample 14 - 229 reads in 170 unique sequences.
## Sample 15 - 512 reads in 318 unique sequences.
## Sample 16 - 481 reads in 353 unique sequences.
## Sample 17 - 634 reads in 364 unique sequences.
## Sample 18 - 607 reads in 286 unique sequences.
## Sample 19 - 614 reads in 332 unique sequences.
## Sample 20 - 598 reads in 338 unique sequences.
## Sample 21 - 539 reads in 317 unique sequences.
## Sample 22 - 561 reads in 310 unique sequences.
## Sample 23 - 599 reads in 327 unique sequences.
## Sample 24 - 598 reads in 373 unique sequences.
## Sample 25 - 567 reads in 288 unique sequences.
## Sample 26 - 650 reads in 243 unique sequences.
## Sample 27 - 607 reads in 310 unique sequences.
## Sample 28 - 526 reads in 336 unique sequences.
## Sample 29 - 551 reads in 308 unique sequences.
## Sample 30 - 571 reads in 351 unique sequences.
## Sample 31 - 482 reads in 276 unique sequences.
## Sample 32 - 644 reads in 343 unique sequences.
## Sample 33 - 558 reads in 283 unique sequences.
## Sample 34 - 453 reads in 278 unique sequences.
## Sample 35 - 446 reads in 313 unique sequences.
## Sample 36 - 521 reads in 313 unique sequences.
## Sample 37 - 517 reads in 309 unique sequences.
## Sample 38 - 447 reads in 311 unique sequences.
## Sample 39 - 500 reads in 332 unique sequences.
## Sample 40 - 369 reads in 272 unique sequences.
## Sample 41 - 359 reads in 251 unique sequences.
## Sample 42 - 540 reads in 346 unique sequences.
## Sample 43 - 501 reads in 292 unique sequences.
## Sample 44 - 391 reads in 278 unique sequences.
## Sample 45 - 516 reads in 331 unique sequences.
## Sample 46 - 422 reads in 301 unique sequences.
## Sample 47 - 567 reads in 340 unique sequences.
## Sample 48 - 521 reads in 348 unique sequences.
## Sample 49 - 599 reads in 354 unique sequences.
## Sample 50 - 472 reads in 330 unique sequences.
## Sample 51 - 571 reads in 335 unique sequences.
## Sample 52 - 416 reads in 259 unique sequences.
## Sample 53 - 525 reads in 306 unique sequences.
## Sample 54 - 559 reads in 272 unique sequences.
## Sample 55 - 593 reads in 321 unique sequences.
## Sample 56 - 537 reads in 329 unique sequences.
## Sample 57 - 470 reads in 276 unique sequences.
## Sample 58 - 644 reads in 343 unique sequences.
## Sample 59 - 595 reads in 328 unique sequences.
## Sample 60 - 561 reads in 342 unique sequences.
## Sample 61 - 551 reads in 352 unique sequences.
## Sample 62 - 544 reads in 324 unique sequences.
## Sample 63 - 532 reads in 295 unique sequences.
## Sample 64 - 372 reads in 221 unique sequences.
## Sample 65 - 558 reads in 341 unique sequences.
## Sample 66 - 581 reads in 330 unique sequences.
## Sample 67 - 431 reads in 331 unique sequences.
## Sample 68 - 356 reads in 246 unique sequences.
## Sample 69 - 403 reads in 297 unique sequences.
## Sample 70 - 387 reads in 273 unique sequences.
## Sample 71 - 400 reads in 295 unique sequences.
## Sample 72 - 560 reads in 353 unique sequences.
## Sample 73 - 457 reads in 329 unique sequences.
## Sample 74 - 594 reads in 347 unique sequences.
##    selfConsist step 2 
##    selfConsist step 3 
##    selfConsist step 4 
## Convergence after  4  rounds.
## Total reads used:  37727
#multithread=TRUE

Le nombre minimum de séquences à utiliser pour l’apprentissage des taux d’erreur peut être précisé avec le paramètre nreads.

The minimum number of sequences to use for error rate learning can be specified with the nreads parameter.


Visualisation du taux d’erreur / Error rate visualization

plotErrors(errF, nominalQ=TRUE)

plotErrors(errR, nominalQ=TRUE)

Les taux d’erreur pour chaque transition (A->C, A->G,…) sont affichés. Chaque point est un taux d’erreur observé pour chaque score de qualité consensuel. La ligne noire montre l’erreur après convergence. La ligne rouge montre l’erreur sous la définition nominale de la valeur Q (pour la technologie Illumina).

The error rates for each possible transition (eg. A->C, A->G, …) are shown. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence. The red line shows the error rates expected under the nominal definition of the Q-value (for Illumina technology).


Déreplication / Dereplicating


Dans cette étape, toutes les séquences identiques vont être regroupées en séquences uniques auxquelles sont attribuées des abondances. Cela va diminuer les temps de calcul subséquants en éliminant des comparaisons redondantes. Les séquences dérépliquées prennent le nom des échantillons d’où elles proviennent.

Combines all identical sequencing reads into into unique sequences with a corresponding abundance. It will reduce subsequent computation time by eliminating redundant comparisons. The dereplicated sequences take the name of the samples from which they come.

derepFs <- derepFastq(filtFs)
names(derepFs) <- sample.names

derepRs <- derepFastq(filtRs)
names(derepRs) <- sample.names

L’avantage de DADA2 réside dans la conservation d’un résumé des informations de qualité associées à chaque séquence unique. Le profil de qualité consensuel d’une séquence unique est la moyenne des qualités de position des lectures dérépliquées. Ces profils de qualité informent le modèle d’erreur de l’étape suivante d’inférence d’échantillon, ce qui augmente considérablement la précision de DADA2.

The advantage of DADA2 lies in the fact that it retains a summary of the quality information associated with each unique sequence. The consensus quality profile of a unique sequence is the average of the positional qualities from the dereplicated reads. These quality profiles inform the error model of the subsequent sample inference step, significantly increasing DADA2’s accuracy.

Unique sequences

Unique sequences


Inférence des échantillons / Sample Inference


dadaFs <- dada(derepFs, 
               err = errF, 
               #multithread=TRUE,
               pool=TRUE)
## 74 samples were pooled: 37727 reads in 20364 unique sequences.
dadaRs <- dada(derepRs, 
               err=errR,
               #multithread=TRUE,
               pool=TRUE)
## 74 samples were pooled: 37727 reads in 18908 unique sequences.
dadaFs[[1]]
## dada-class: object describing DADA2 denoising results
## 76 sample sequences were inferred from 364 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, BAND_SIZE = 16, USE_QUALS = TRUE
dadaRs[[1]]
## dada-class: object describing DADA2 denoising results
## 77 sample sequences were inferred from 340 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, BAND_SIZE = 16, USE_QUALS = TRUE
#save(dadaRs, file="data/dadaRs.rdata")
#save(dadaFs, file="data/dadaFs.rdata")


Fusion des séquences pairées / Merging paired reads


FUSION !

FUSION !


Tout l’intérêt du séquençage en paire réside dans le but de fusionner les deux brins afin d’accroitre notre confiance en leur fiabilité. La fusion permet également d’obtenir des amplicons plus long.
La fonction mergePairs nécessite qu’on lui fournisse les objets calculés dans les deux étapes précédentes (derep et dada). Puis, les paramètres que nous pouvons modifiés librement sont:

The whole point of paired-end sequencing lies in the goal of merging the two strands to increase our confidence in their reliability. Merging also makes it possible to obtain longer amplicons.
The function mergePairs needs to be provided with the objects computed in the two preceding stages (derep and dada). Then, the parameters we can freely modify are:

Other settings can also be changed, they are accessible on the help page of the function : ?mergePairs. For example, if returnRejects = TRUE, pairs that were rejected because of mismatches in the overlap region are kept in the output.

mergers <- mergePairs(dadaFs, derepFs, dadaRs, derepRs, 
                      minOverlap = 12, 
                      maxMismatch = 0)


Examinons les résultats! / Let’s inspect the results! 

head(mergers[[1]])
##                                                                                                                                                                                                                                                                                                                                                                                  sequence
## 1    ATGCGATACGTAATGTGAATTGCAGAATTCAGTGAATCATCGAATCTTTGAACGCACCTTGCGCCCCTTGGTATTCCGAGGGGCACACCCGTTTGAGTGTCGTGAATACTCTCAACCTTCTTGGTTTCTTTGACCACGAAGGCTTGGACTTTGGAGGTTTTTCTTGCTGGCCTCTTTAGAAGCCAGCTCCTCCTAAATGAATGGGTGGGGTCCGCTTTGCTGATCCTCGACGTGATAAGCATCTCTTCTACGTCTCAGTGTCAGCTCGGAACCCCGCTTTCCAACCGTCTTTGGACAAAGACAATGTTCGAGTTGCGACTCGACCTTACAAACCTTGACCTCAAATCGGGTGAGACTACCCGCTGAACTTAA
## 2                                                                                    ATGCGATAAGTAGTGTGAATTGCAGAATTCAGTGAATCATCGAATCTTTGAACGCACATTGCGCCCCTTGGTATCCCGAGGGGCATGCCTGTTCGAGCGTCATTTCACCACTCAAGCCTGGCTTGGTGTTGGGCGACGTCCCCTTTTGGGGACGCGTCTCGAAACGCTCGGCGGCGTGGCACCGGCTTTAAGCGTAGCAGAATCTTTCGCTTTGAAAGTCGGGGCCCCGTCTGCCGGAAGACCTACTCGCAAGGTTGACCTCGGATCAGGCAGGGATACCCGCTGAACTTAA
## 3                                                                                        ATGCGATAAGTAATGTGAATTGCAGAATTCAGTGAATCATCGAATCTTTGAACGCACATTGCGCCCCTTGGTATTCCGAGGGGCATGCCTGTTCGAGCGTCATTATAACCACTCAAGCCCCGGCTTGGTCTTGGGGTTCGCGGTCCGCGGCCCTTAAACTCAGTGGCGGTGCCGTCTGGCTCTAAGCGCAGTAATTCTCTCGCTATAGTGTCTAGGTGGTTGCTTGCCATAATCCCCCAATTTTTTACGGTTGACCTCGGATCAGGTAGGGATACCCGCTGAACTTAA
## 4                                 ATGCGATACGTAATGTGAATTGCAGAATTCAGTGAATCATCGAATCTTTGAACGCACATTGCACTCCTTGGTATTCCGAGGAGTATGCCTGTTTCAGTATCATGAGCACTCTCACACCTAACCTTTGGGTTTATGGCGTGGAATTGGAATGCGCCGACTGTCATGGTTGGCCCTTCTAAAATGTAGTTCTTGGCTGTCACCTAATACAGCAGTTTGGCCTAATAGTTTTGGCATTCATTGTCAAATCTTTGGCTAACATTTGCTCCAGGAGTCAGTCTTGATAATACAGAAAACTCATTCAAATTTTGATCTGAAATCAGGTAGGGCTACCCGCTGAACTTAA
## 5 ATGCGATACGTAATGTGAATTGCAGAATTCCGTGAATCATTGAATCTTTGAACGCATCTTGCGCCTCTTGGTATTCCGAGGGGCATGCCTGTTTGAGTGTCATTAGAACTATCAAAAAAATAGATGATTTCAATCGTTAATTTTTTTGGAATTGGAGGTGGTGCTGGTCTTTTTCCATTAATGGCCCAAGCTCCTCCGAAATGCATTAGCGAATGCAGTGCACTTTTTCTCCTTGCTTTTTCTGGGCATTGATAGTTTACTCTCATGCCCTAAGCTGGTAGGGAGGAAGTCACAGAATGCTTCCCGCTCCTGAATGTAATACAAAACTTGACGATCAAACCCCTCAAATCAGGCAGGACTACCCGCTGAACTTAA
## 6                                                                        ATGCGATAAGTAATGCGAATTGCAGAATTCAGTGAGTCATCGAATCTTTGAACGCATATTGCGCCCTTTGGTATTCCGAAGGGCATGCCTGTTCGAGCGTCATGATCAACCATCAAGCCTGGCTTGTCGTTGGACCCTGTTGTCTCTGGGCGACAGGTCCGAAAGATAATGACGGTGTCATGGCAACCCCGAATGCAACGAGCTTTTTTATAGGCACGCATTTAGTGGTTGGCAAGGCCCCCTCGTGCGTTATTATTTTCTTACGGTTGACCTCGGATCAGGTAGGAATACCCGCTGAACTTAA
##   abundance forward reverse nmatch nmismatch nindel prefer accept
## 1       172      15      12    150         0      0      2   TRUE
## 2        43       8       9    230         0      0      1   TRUE
## 3        37      22      18    234         0      0      2   TRUE
## 4        34       3       2    179         0      0      2   TRUE
## 5        32       4       3    147         0      0      1   TRUE
## 6        21      12      10    218         0      0      1   TRUE
max(mergers[[1]]$nmatch) # Largest overlap 
## [1] 257
min(mergers[[1]]$nmatch) # Smallest overlap           
## [1] 113


Tableau des ASVs / ASVs table


Nous avons enfin nos ASVs que nous allons stoquer dans l’objet seqtab.

We finally have our ASVs which we are going to store in the seqtab object. 

seqtab <- makeSequenceTable(mergers)
## The sequences being tabled vary in length.
dim(seqtab)
## [1]  74 551
seqtab[,1]
##  S1  S2  S3  S4  S5  S6  S7  S8  S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 
##   3   5   2   2   2   1   1   0   3   0   2   0   2   3   1  48 206 129 
## S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S33 S34 S35 S36 S37 S39 
##   1   1   1   1   3   2   0 532   1   7  81  11   2   0   1   1   2   9 
## S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S58 S59 
##   4   2   1   4   6   3   0   5   0   2   6   4   2   2   3   0   0   0 
## S60 S61 S62 S63 S64 S65 S66 S67 S68 S69 S72 S73 S74 S75 S76 S77 S78 S79 
##   2   4   9   0   1   5   1   0   3   4   2   4   5   0   3  11   2   8 
## S80 S81 
##   9   1

Nous obtenons 551 ASVs à partir des 74000 séquences brutes que nous avions au début.
seqtab[,1] renseigne le nombre de fois qu’on retrouve la séquence du premier ASV dans chaque échantillon.

We get 551 ASVs from the 74000 raw sequences we had at the beginning. seqtab [, 1] gives the number of times the first ASV’s sequence is found in each sample.

Inspectons la longueur de nos ASVs / Let’s inspect the ASVs’ lengths


hist(nchar(getSequences(seqtab)),xlab="Size", ylab="Frequency", main = "ASVs length", xlim=c(250,450), ylim=c(0,250))


Suppression des chimères / Removing chimeras



Cette étape vise à éliminer toutes les séquences non biologiques, les échantillons du tableau des ASV sont regroupés (method=“pooled”) pour l’identification. D’autres méthodes peuvent être utilisées comme la méthode consensus où chaque échantillon est vérifié individuellement pour identifier les bimères.

This step aims at removing all non-biological sequences, the samples in the ASV table are all pooled together for bimera identification. Other methods can be used like the consensus method where samples are checked individually for bimeras.

seqtab.nochim <- removeBimeraDenovo(seqtab, 
                                    method = "pooled", 
                                    #multithread = TRUE,
                                    verbose = TRUE) 
## Identified 5 bimeras out of 551 input sequences.
#save(seqtab.nochim, file="data/seqtab.nochim.rdata")


Examinons les résultats ! / Let’s inspect the results !


round((sum(seqtab.nochim)/sum(seqtab)*100),2) # Percentage of the total sequence reads
## [1] 99.41
hist(nchar(getSequences(seqtab.nochim)),xlab="Size", ylab="Frequency", main = "Non-chimeric ASVs length", xlim=c(250,450), ylim=c(0,250)) # Lenght of the non-chimeric sequences

Maintenant nous pouvons transformer les occurences des ASVs en présence / absence. ce qui va permettre quantifie le nombre d’ASVs par échantillons.

Now we can transform the ASVs occurrences in presence / absence which will allow to quantify the number of ASVs per sample.

seqtab.nochim.bin <- ifelse(seqtab.nochim>0,1,0)


Tableau de suivi / Track table


Le tableau qui suit permet de voir combien de séquences ont été éliminées à chaque étape. Une chute trop grande du nombre de séquences peut indiquer un problème. Par exemple, si peu de séquences passent l’étape de la suppresion des bimères cela peu indiquer qu’il reste des bouts d’amorces avec de nucléotides ambigües.

The following table shows how many sequences were eliminated at each step. An excessive loss in the number of sequences may indicate a problem. For example, if too few sequences pass the bimera removal step, it may indicate that there are still bits of primers with ambiguous nucleotides.

getN <- function(x) sum(getUniques(x))
track <- cbind(out, # input & filtered
               round(((out[,2]/out[,1])*100),2), # % (filtered / input) 
               sapply(mergers, getN), # Merged 
               round(((sapply(mergers, getN)/out[,1])*100),2), # % (Merged / Input)
               rowSums(seqtab.nochim),# Non-chimeric
               round(((rowSums(seqtab.nochim)/out[,1])*100),2),# % (Non-chimeric / Input)
               rowSums(seqtab.nochim.bin)) # Number of ASVs per sample 

colnames(track) <- c("Input", 
                    "Filter", 
                    "%Filt/In",
                    "Merge",
                    "%Mer/In", 
                    "Non-chim",
                    "%Non-chim/In",
                    "#ASV/sample") # Column names
rownames(track) <- sample.names # Row names
track<-as.data.frame(track)
head(track)
##    Input Filter %Filt/In Merge %Mer/In Non-chim %Non-chim/In #ASV/sample
## S1  1000    595     59.5   576    57.6      576         57.6          68
## S2  1000    482     48.2   431    43.1      427         42.7          37
## S3  1000    556     55.6   481    48.1      481         48.1          30
## S4  1000    425     42.5   373    37.3      372         37.2          52
## S5  1000    387     38.7   328    32.8      327         32.7          59
## S6  1000    619     61.9   597    59.7      597         59.7          24


Assignation taxonomique / Taxonomy assignment


Nous voyons enfin le bout du pipeline avec cette importante étape d’assignation taxonomique. Grâce à l’implémentation de la méthode de classification naïve bayésienne, la fonction assignTaxonomy prend en entrée l’ensemble de séquences à classer ainsi qu’un ensemble de séquences de référence avec une taxonomie connue. Les assignations taxonomiques sont données avec une confiance minimale de bootstrap spécifié avec le paramètre minBoot. La base de données de références (UNITE) est accessible sur ce lien https://unite.ut.ee/repository.php. D’autres bases de données sont également disponibles.

We are finally going to the end of the pipeline with this important step of taxonomic assignment. Thanks to the implementation of the naïve Bayesian classification method, the assignTaxonomy function takes as input all the sequences to be classified as well as reference set of sequences with known taxonomy. The taxonomic assignments are given with a minimum bootstrap confidence specified with the minBoot parameter. The database of references (UNITE) is accessible on this link https://unite.ut.ee/repository.php. Other databases are also available. 

taxotab <- assignTaxonomy(seqtab.nochim,
                          refFasta = "reference_database/sh_general_release_dynamic_01.12.2017.fasta",
                          minBoot = 50, #Default 50. The minimum bootstrap #confidence for # assigning a taxonomic level.
                          multithread=TRUE)
## UNITE fungal taxonomic reference detected.
save(taxotab, file = "data/taxotab.rdata")
#load("data/taxotab.rdata")


Examinons les résultats ! / Let’s inspect the results ! 

# view(taxotab) # Full taxonomy table 
write.table(taxotab[1:5,], row.names = FALSE) # First 5 ASVs' taxonomy without the ASV sequence
## "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "Species"
## "k__Fungi" "p__Basidiomycota" "c__Agaricomycetes" "o__Agaricales" "f__Tricholomataceae" "g__Mycena" NA
## "k__Plantae" NA NA NA NA NA NA
## "k__Fungi" "p__Mucoromycota" "c__Umbelopsidomycetes" "o__Umbelopsidales" "f__Umbelopsidaceae" "g__Umbelopsis" "s__dimorpha"
## "k__Fungi" "p__Ascomycota" "c__Dothideomycetes" "o__Mytilinidales" "f__Gloniaceae" "g__Cenococcum" "s__geophilum"
## "k__Fungi" "p__Basidiomycota" "c__Agaricomycetes" "o__Agaricales" "f__Hygrophoraceae" "g__Hygrocybe" "s__flavescens"
unique(unname(taxotab[,7])) # Number of unique species
##   [1] NA                    "s__dimorpha"         "s__geophilum"       
##   [4] "s__flavescens"       "s__algarvense"       "s__terricola"       
##   [7] "s__punicea"          "s__elongatum"        "s__humilis"         
##  [10] "s__sylvestris"       "s__opacum"           "s__subvinosa"       
##  [13] "s__abramsii"         "s__rexiana"          "s__ericae"          
##  [16] "s__terminalis"       "s__chlamydosporicum" "s__variata"         
##  [19] "s__conica"           "s__zollingeri"       "s__asperellum"      
##  [22] "s__album"            "s__kathiae"          "s__deciduus"        
##  [25] "s__trabinellum"      "s__saponaceum"       "s__rufescens"       
##  [28] "s__populi"           "s__auratus"          "s__reidii"          
##  [31] "s__podzolica"        "s__brunneoviolacea"  "s__simile"          
##  [34] "s__subsulphurea"     "s__heterochroma"     "s__pseudozygospora" 
##  [37] "s__microspora"       "s__camphoratus"      "s__var._bulbopilosa"
##  [40] "s__acicola"          "s__verrucosa"        "s__miniata"         
##  [43] "s__fuckelii"         "s__maius"            "s__bicolor"         
##  [46] "s__paczoskii"        "s__splendens"        "s__echinulatum"     
##  [49] "s__chlorophana"      "s__australis"        "s__lignicola"       
##  [52] "s__changbaiensis"    "s__cantharellus"     "s__lubrica"         
##  [55] "s__phyllophila"      "s__fibrillosa"       "s__pygmaeum"        
##  [58] "s__isabellina"       "s__fuscella"         "s__fragilis"        
##  [61] "s__asperulatus"      "s__carneum"          "s__bulbillosa"      
##  [64] "s__spirale"          "s__difforme"         "s__nigrella"        
##  [67] "s__myriocarpa"       "s__reginae"          "s__finlandica"      
##  [70] "s__smithii"          "s__fellea"           "s__vrijmoediae"     
##  [73] "s__metachroides"     "s__lacmus"           "s__spurius"         
##  [76] "s__arachnoidea"      "s__dioscoreae"       "s__fusiformis"      
##  [79] "s__laetior"          "s__coccinea"         "s__spadicea"        
##  [82] "s__foliicola"        "s__pyriforme"        "s__macrocystis"     
##  [85] "s__risigallina"      "s__pura"             "s__cinereus"        
##  [88] "s__rebaudengoi"      "s__diversispora"     "s__hymenocystis"    
##  [91] "s__fumosa"           "s__sublilacina"      "s__atrovirens"      
##  [94] "s__rufum"            "s__mors-panacis"     "s__skinneri"        
##  [97] "s__pullulans"        "s__delicatulum"      "s__cygneicollum"    
## [100] "s__acerinum"         "s__glacialis"        "s__crocea"          
## [103] "s__oblongisporum"    "s__globulifera"      "s__silvestris"      
## [106] "s__variabilis"       "s__pilosella"        "s__griseoviride"    
## [109] "s__moravica"         "s__nitrata"          "s__sindonia"        
## [112] "s__piceae"           "s__sphaeroides"      "s__stuposa"         
## [115] "s__renispora"        "s__honrubiae"        "s__grovesii"        
## [118] "s__anomalovelatus"   "s__flavidum"         "s__miyagiana"       
## [121] "s__boeremae"         "s__glutinosum"       "s__fallax"          
## [124] "s__falcata"          "s__rhododendri"      "s__fusispora"       
## [127] "s__scaurus"          "s__schulzeri"        "s__lauri"           
## [130] "s__serotinus"        "s__alliacea"         "s__cylichnium"      
## [133] "s__alpina"           "s__miyabei"          "s__sepiacea"        
## [136] "s__rosea"            "s__spinosum"         "s__rimosissimus"    
## [139] "s__albicastaneus"    "s__acerina"          "s__fumosibrunneus"  
## [142] "s__hyalocuspica"     "s__physodes"         "s__epicalamia"      
## [145] "s__terrestris"       "s__bombacina"        "s__entomopaga"      
## [148] "s__xylopini"         "s__umbrosum"         "s__physaroides"

Nous obtenons 546 ASV différents dont 149 identifiés à l’espèce.

We obtained 546 different ASVs of which 149 have been identified at the species level

CHALLENGE

Pouvez-vous trouvez le nombre de famille différentes?
Can you find the number of different families?

Nota Bene